Seismic risk assessment of high-voltage transformers using Bayesian belief networks

Siraj, Tammeen; Tesfamariam, Solomon; Dueñas‐Osorio, Leonardo

doi:10.1080/15732479.2014.921826

Cited by 17 publications

(3 citation statements)

References 64 publications

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“…DAGs are graphs, where the links have directions and don't form cycles, whereas the nodes represent the random variables of interest, and each node is associated with several possible states (Siraj et al, 2014). The relationships between the nodes in the BBN are demonstrated in the form of family relationships, whereas 𝑋 and 𝑌 are regarded as parents of 𝑍, and 𝑍 is considered as the child of both 𝑋 and 𝑌 if a link goes from 𝑋 to 𝑍, and 𝑌 to 𝑍.…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

“…The relationships between the nodes in the BBN are demonstrated in the form of family relationships, whereas 𝑋 and 𝑌 are regarded as parents of 𝑍, and 𝑍 is considered as the child of both 𝑋 and 𝑌 if a link goes from 𝑋 to 𝑍, and 𝑌 to 𝑍. The steps of formulating the BBN are as follows (Siraj et al, 2014):…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

“…There are several approaches to estimate the missing conditional probabilities such as assigning an equal probability equals to 1/𝑛 to each state, whereas 𝑛 indicates the number of states. Another approach is to assume a uniform distribution for the unknown conditional probabilities (Siraj et al, 2014). Pendharkar (2008) addressed the problem of filling-in missing conditional probabilities using the maximum entropy approach.…”

Section: Unknown Conditional Probabilitiesmentioning

confidence: 99%

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A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

Abdelkader

Zayed

Marzouk

2019

Structure and Infrastructure Engineering

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Bridges are aging and deteriorating. Reliable deterioration modeling is regarded as one of the vital components of Bridge management systems. This paper presents an automated defectbased tool to predict the future condition of the bridge decks by calibrating the Markovian model based on a hybrid Bayesian-optimization approach. The in-state probabilities are demonstrated in the form of posterior distributions, whereas the transition from a condition state to the next lower state is a function of the severities of five types of bridge defects. In the present study, the Bayesian belief network is employed to construct the likelihood function by modeling the dependencies between the bridge defects. The maximum entropy optimization is incorporated to compute the missing conditional probabilities. The proposed approach utilizes Markov chain Monte Carlo Metropolis-Hastings algorithm to derive the posterior distributions. Finally, a stochastic optimization model is designed to build a variable transition probability matrix for each five-year zone via genetic algorithm. An automated tool is programmed using C#.net programming language to facilitate the implementation of the developed deterioration model by the users. Results show that the proposed model outperformed some commonly-utilized deterioration models as per three performance indicators which are: root-mean squared error, mean absolute error, chi-squared statistic.

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Section: Bayesian Belief Networkmentioning

confidence: 99%

Section: Bayesian Belief Networkmentioning

confidence: 99%

Section: Unknown Conditional Probabilitiesmentioning

confidence: 99%

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A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

Abdelkader

Zayed

Marzouk

2019

Structure and Infrastructure Engineering

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Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Franchin

Lupoi

Noto³

et al. 2015

Earthq Engng Struct Dyn

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Summary Infrastructure owners and operators, or governmental agencies, need rapid screening tools to prioritize detailed risk assessment and retrofit resources allocation. This paper provides one such tool, for use by highway administrations, based on Bayesian belief network (BBN) and aimed at replacing so‐called generic or typological seismic fragility functions for reinforced concrete girder bridges. Resources for detailed assessments should be allocated to bridges with highest consequence of damage, for which site hazard, bridge fragility, and traffic data are needed. The proposed BBN based model is used to quantify seismic fragility of bridges based on data that can be obtained by visual inspection and engineering drawings. Results show that the predicted fragilities are of sufficient accuracy for establishing relative ranking and prioritizing. While the actual data and seismic hazard employed to train the network (establishing conditional probability tables) refer to the Italian bridge stock, the network structure and engineering judgment can easily be adopted for bridges in different geographical locations. Copyright © 2015 John Wiley & Sons, Ltd.

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Double friction pendulum-based isolation optimization for transformer-bushing systems

Li,

Xie,

Wen

2024

Engineering Structures

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Seismic risk assessment of high-voltage transformers using Bayesian belief networks

Cited by 17 publications

References 64 publications

A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

A computerized hybrid Bayesian-based approach for modelling the deterioration of concrete bridge decks

Seismic fragility of reinforced concrete girder bridges using Bayesian belief network

Double friction pendulum-based isolation optimization for transformer-bushing systems

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